Comparing Fairness of Generative Mobility Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912108488163328 |
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| author | Wang, Daniel McFarland, Jack Mashhadi, Afra Ugurel, Ekin |
| author_facet | Wang, Daniel McFarland, Jack Mashhadi, Afra Ugurel, Ekin |
| contents | This work examines the fairness of generative mobility models, addressing the often overlooked dimension of equity in model performance across geographic regions. Predictive models built on crowd flow data are instrumental in understanding urban structures and movement patterns; however, they risk embedding biases, particularly in spatiotemporal contexts where model performance may reflect and reinforce existing inequities tied to geographic distribution. We propose a novel framework for assessing fairness by measuring the utility and equity of generated traces. Utility is assessed via the Common Part of Commuters (CPC), a similarity metric comparing generated and real mobility flows, while fairness is evaluated using demographic parity. By reformulating demographic parity to reflect the difference in CPC distribution between two groups, our analysis reveals disparities in how various models encode biases present in the underlying data. We utilized four models (Gravity, Radiation, Deep Gravity, and Non-linear Gravity) and our results indicate that traditional gravity and radiation models produce fairer outcomes, although Deep Gravity achieves higher CPC. This disparity underscores a trade-off between model accuracy and equity, with the feature-rich Deep Gravity model amplifying pre-existing biases in community representations. Our findings emphasize the importance of integrating fairness metrics in mobility modeling to avoid perpetuating inequities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparing Fairness of Generative Mobility Models Wang, Daniel McFarland, Jack Mashhadi, Afra Ugurel, Ekin Machine Learning I.6.4; I.2.6; K.4.1 This work examines the fairness of generative mobility models, addressing the often overlooked dimension of equity in model performance across geographic regions. Predictive models built on crowd flow data are instrumental in understanding urban structures and movement patterns; however, they risk embedding biases, particularly in spatiotemporal contexts where model performance may reflect and reinforce existing inequities tied to geographic distribution. We propose a novel framework for assessing fairness by measuring the utility and equity of generated traces. Utility is assessed via the Common Part of Commuters (CPC), a similarity metric comparing generated and real mobility flows, while fairness is evaluated using demographic parity. By reformulating demographic parity to reflect the difference in CPC distribution between two groups, our analysis reveals disparities in how various models encode biases present in the underlying data. We utilized four models (Gravity, Radiation, Deep Gravity, and Non-linear Gravity) and our results indicate that traditional gravity and radiation models produce fairer outcomes, although Deep Gravity achieves higher CPC. This disparity underscores a trade-off between model accuracy and equity, with the feature-rich Deep Gravity model amplifying pre-existing biases in community representations. Our findings emphasize the importance of integrating fairness metrics in mobility modeling to avoid perpetuating inequities. |
| title | Comparing Fairness of Generative Mobility Models |
| topic | Machine Learning I.6.4; I.2.6; K.4.1 |
| url | https://arxiv.org/abs/2411.04453 |